DEEP NEURAL NETWORK-2020


A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers. The DNN finds the correct mathematical manipulation to turn the input into the output, whether it be a linear relationship or a non-linear relationship.

Deep ReLU Neural Network Expression Rates for Data-to-QoI Maps in Bayesian PDE Inversion
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For Bayesian inverse problems with input-to-response maps given by well-posed partial differential equations (PDEs) and subject to uncertain parametric or function space input, we establish (under rather weak conditions on the forward , input-to-response maps) the

Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network
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Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore

Bleeding Classification of Enhanced Wireless Capsule Endoscopy Images using Deep Convolutional Neural Network
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Recently, many research works on the Wireless Capsule Endoscopy (WCE) device have been published. This includes computer aided decision in capsule endoscopy, electronic system etc. WCE is a procedure to examine internal organ using a small camera that can

Deep Neural Network Acceleration Based on Low-Rank Approximated Channel Pruning
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Acceleration and compression on deep Convolu-tional Neural Networks (CNNs) have become a critical problem to develop intelligence on resource-constrained devices. Previous channel pruning can be easily deployed and accelerated without specialized

SEMIGLOBAL OPTIMAL FEEDBACK STABILIZATION OF AUTONOMOUS SYSTEMS VIA DEEP NEURAL NETWORK APPROXIMATION
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A learning approach for optimal feedback gains for nonlinear continuous time control systems is proposed and analysed. The goal is to establish a rigorous framework for computing approximating optimal feedback gains using neural networks. The approach

Identification of Wild Species in Texas from Camera-trap Images using Deep Neural Network for Conservation Monitoring
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Protection of endangered species requires continuous monitoring and updated information about the existence, location, and behavioral alterations in their habitat. Remotely activated camera or camera traps is a reliable and effective method of photo documentation of local

A Modified Deep Convolutional Neural Network for Brian Abnormalities Detection
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Cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging

Search for flavour-changing neutral currents in processes with a single top quark in association with a photon using a deep neural network at the ATLAS
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In this thesis, a search for flavour-changing neutral currents in processes involving a singly produced top quark and a photon is presented. In this search, proton-proton collision data are used which were collected by the ATLAS experiment at the LHC and correspond to an

DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features
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Abstract Background: Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the

Deep Convolutional Neural Network Design Approach for 3D Object Detection for Robotic Grasping
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Automation is increasing with the advent of 3D-and depth images using an infrared camera. Formerly, most of the object detection and recognition were done by a 2D-camera of Red- Green-Blue (RGB) images. Today, with the availability of economical 3D-sensors, people


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